changes
This commit is contained in:
Tim
140
compare_models_2k.py
Normal file
140
compare_models_2k.py
Normal file
@@ -0,0 +1,140 @@
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn import set_config
|
||||
|
||||
from sklearn.compose import ColumnTransformer
|
||||
from sklearn.preprocessing import FunctionTransformer, MultiLabelBinarizer
|
||||
import ast
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.multioutput import MultiOutputClassifier
|
||||
from sklearn.metrics import classification_report
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.datasets import load_iris
|
||||
from sklearn.metrics import accuracy_score, classification_report
|
||||
from sklearn.svm import SVC, LinearSVC
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
|
||||
from sklearn.linear_model import LogisticRegression, RidgeClassifier, PassiveAggressiveClassifier, Perceptron, SGDClassifier
|
||||
from sklearn.neighbors import KNeighborsClassifier, NearestCentroid, RadiusNeighborsClassifier
|
||||
from sklearn.svm import SVC
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier, VotingClassifier, StackingClassifier
|
||||
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB, ComplementNB
|
||||
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
|
||||
from sklearn.dummy import DummyClassifier
|
||||
from sklearn.neural_network import MLPClassifier
|
||||
|
||||
set_config(transform_output="pandas") # dataframe supremacy
|
||||
|
||||
jobs = 12
|
||||
max_iter = 3000
|
||||
|
||||
def prepDataset(dataset): #returns X_train, X_test, y_train, y_test
|
||||
dataset = pd.read_csv(dataset,sep=",")
|
||||
# desc, genres, tags
|
||||
column_transformer = ColumnTransformer([
|
||||
# merge all descriptions
|
||||
('desc', FunctionTransformer(lambda X: X.fillna('').agg(' '.join, axis=1).to_frame(name="desc")),
|
||||
['detailed_description', 'about_the_game', 'short_description']),
|
||||
('pass', 'passthrough', ['genres']),#, 'tags'
|
||||
],
|
||||
verbose_feature_names_out=False
|
||||
)
|
||||
dataset = column_transformer.fit_transform(dataset)
|
||||
#### SET MISSING VALUES
|
||||
print("SETMISS")
|
||||
# Setting missing numeric values to the mean
|
||||
dataset.fillna(dataset.mean(numeric_only=True), inplace=True)
|
||||
# Setting missing text values to 'Unknown'
|
||||
dataset.fillna('', inplace=True)
|
||||
# Setting missing values in other columns to NaN
|
||||
dataset.dropna(inplace=True)
|
||||
##### STRUCTURIZE GENRES to onehot
|
||||
#serialize array
|
||||
dataset['genres'] = dataset['genres'].map(lambda s: ast.literal_eval(s))
|
||||
#print(dataset['genres']) # in py but not yet onehotenc
|
||||
# MultiLabelBinarizer does onehotenc for arrays
|
||||
mlb_genres = MultiLabelBinarizer()
|
||||
genres_encoded = mlb_genres.fit_transform(dataset.pop('genres'))
|
||||
#genres_count = len(mlb_genres.classes_) # for multi-label classifiction later
|
||||
genres_df = pd.DataFrame(genres_encoded, columns=mlb_genres.classes_)
|
||||
#print(genres_df)
|
||||
#dataset = pd.concat([dataset, genres_df], axis=1)
|
||||
#print(dataset)
|
||||
#### convert text to bag of words
|
||||
## Count vs Tfidf vectorizer
|
||||
vectorizer = TfidfVectorizer()
|
||||
tfidf_matrix = vectorizer.fit_transform(dataset['desc']) # matrix
|
||||
tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
|
||||
#print(tfidf_df)
|
||||
##### MODEL
|
||||
print("MODEL")
|
||||
X = tfidf_df
|
||||
y = genres_df
|
||||
# cleanup datapoints that dont have a target value (all target columns are 0)
|
||||
mask = y.sum(axis=1).map(lambda x: x > 0)
|
||||
#print((mask == False).sum()) #31 cases with all target columns 0
|
||||
X_clean = X[mask]
|
||||
y_clean = y[mask]
|
||||
# Split dataset
|
||||
return train_test_split(X_clean, y_clean, random_state=0)
|
||||
def comparison(X_train, X_test, y_train, y_test, estimator,): #returns class_report
|
||||
multi_target_clf = MultiOutputClassifier(estimator, n_jobs=jobs) # LogisticRegression(max_iter=1337, random_state=0)
|
||||
# model training
|
||||
multi_target_clf.fit(X_train, y_train)
|
||||
# predict against test data
|
||||
y_pred = multi_target_clf.predict(X_test)
|
||||
return classification_report(y_test, y_pred, zero_division=0.0)
|
||||
datasets = [
|
||||
'games_march2025_cleaned_2k.csv',
|
||||
#'games_march2025_cleaned_10k.csv',
|
||||
#'games_march2025_cleaned.csv'
|
||||
]
|
||||
estimators = {
|
||||
"LogisticRegression": LogisticRegression(random_state=0, max_iter=max_iter),
|
||||
"RidgeClassifier": RidgeClassifier(random_state=0, max_iter=max_iter),
|
||||
"PassiveAggressiveClassifier": PassiveAggressiveClassifier(random_state=0, max_iter=max_iter),
|
||||
"Perceptron": Perceptron(random_state=0, max_iter=max_iter),
|
||||
"SGDClassifier": SGDClassifier(random_state=0, max_iter=max_iter),
|
||||
"KNeighborsClassifier": KNeighborsClassifier(),
|
||||
"NearestCentroid": NearestCentroid(),
|
||||
# "RadiusNeighborsClassifier": RadiusNeighborsClassifier(), # failed bcs no neighbours in range :sob:
|
||||
"LinearSVC": LinearSVC(random_state=0, max_iter=max_iter),
|
||||
"SVC": SVC(random_state=0, max_iter=max_iter),
|
||||
"DecisionTreeClassifier": DecisionTreeClassifier(random_state=0),
|
||||
"RandomForestClassifier": RandomForestClassifier(random_state=0),
|
||||
"ExtraTreesClassifier": ExtraTreesClassifier(random_state=0),
|
||||
"BaggingClassifier": BaggingClassifier(random_state=0),
|
||||
"AdaBoostClassifier": AdaBoostClassifier(random_state=0),
|
||||
"GradientBoostingClassifier": GradientBoostingClassifier(random_state=0),
|
||||
"HistGradientBoostingClassifier": HistGradientBoostingClassifier(random_state=0, max_iter=max_iter),
|
||||
"GaussianNB": GaussianNB(),
|
||||
"MultinomialNB": MultinomialNB(),
|
||||
"BernoulliNB": BernoulliNB(),
|
||||
"ComplementNB": ComplementNB(),
|
||||
"LinearDiscriminantAnalysis": LinearDiscriminantAnalysis(),
|
||||
"MLPClassifier": MLPClassifier(random_state=0, max_iter=int(max_iter/5), verbose=True),
|
||||
"DummyClassifier": DummyClassifier(random_state=0)
|
||||
}
|
||||
|
||||
#"VotingClassifier": VotingClassifier(estimators=[('lr', LogisticRegression()), ('rf', RandomForestClassifier())]),
|
||||
#"StackingClassifier": StackingClassifier(estimators=[('lr', LogisticRegression()), ('rf', RandomForestClassifier())]),
|
||||
for dataset in datasets:
|
||||
print("-" * 60)
|
||||
print("dataset -> " + dataset)
|
||||
print("mkdir")
|
||||
folder = dataset.split(".csv")[0]
|
||||
if not os.path.isdir(folder):
|
||||
os.mkdir(folder)
|
||||
X_train, X_test, y_train, y_test = prepDataset(dataset)
|
||||
for esti in estimators:
|
||||
print("model: " + esti)
|
||||
compari = comparison(X_train, X_test, y_train, y_test, estimators[esti])
|
||||
print("open")
|
||||
f = open(folder + "/" + esti +".txt", mode="w+", encoding="utf-8")
|
||||
f.write(compari)
|
||||
print("write")
|
||||
f.close()
|
||||
print("close")
|
||||
Reference in New Issue
Block a user